






"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class       __A		(   SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				DanceDiffusionPipeline
					UpperCAmelCase__								=				UNCONDITIONAL_AUDIO_GENERATION_PARAMS
					UpperCAmelCase__								=				PipelineTesterMixin.required_optional_params - {
					    "callback",
					    "latents",
					    "callback_steps",
					    "output_type",
					    "num_images_per_prompt",
					}
					UpperCAmelCase__								=				UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[Any]:
											torch.manual_seed(0					)
											__magic_name__:	Dict							=       UNetaDModel(
											    block_out_channels=(3_2, 3_2, 6_4)     ,		extra_in_channels=1_6     ,		sample_size=5_1_2     ,		sample_rate=1_6_0_0_0     ,		in_channels=2     ,		out_channels=2     ,		flip_sin_to_cos=__snake_case     ,		use_timestep_embedding=__snake_case     ,		time_embedding_type="""fourier"""     ,		mid_block_type="""UNetMidBlock1D"""     ,		down_block_types=("""DownBlock1DNoSkip""", """DownBlock1D""", """AttnDownBlock1D""")     ,		up_block_types=("""AttnUpBlock1D""", """UpBlock1D""", """UpBlock1DNoSkip""")     ,		)
											__magic_name__:	Tuple							=       IPNDMScheduler()

											__magic_name__:	Union[str, Any]							=       {
											    """unet""": unet,
											    """scheduler""": scheduler,
											}
											return components

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      List[str]=0					)			->							str:
											if str(__snake_case					).startswith("""mps"""					):
																	__magic_name__:	List[str]							=       torch.manual_seed(__snake_case					)
											else:
																	__magic_name__:	Union[str, Any]							=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
											__magic_name__:	int							=       {
											    """batch_size""": 1,
											    """generator""": generator,
											    """num_inference_steps""": 4,
											}
											return inputs

					def   lowerCamelCase__   (			self  :      str					)			->							Optional[int]:
											__magic_name__:	int							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
											__magic_name__:	List[Any]							=       self.get_dummy_components()
											__magic_name__:	Dict							=       DanceDiffusionPipeline(**__snake_case					)
											__magic_name__:	List[str]							=       pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	Union[str, Any]							=       self.get_dummy_inputs(__snake_case					)
											__magic_name__:	Any							=       pipe(**__snake_case					)
											__magic_name__:	int							=       output.audios

											__magic_name__:	Optional[int]							=       audio[0, -3:, -3:]

											assert audio.shape == (1, 2, components["unet"].sample_size)
											__magic_name__:	Optional[Any]							=       np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]					)
											assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1E-2

					@skip_mps
					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[int]:
											return super().test_save_load_local()

					@skip_mps
					def   lowerCamelCase__   (			self  :      int					)			->							Union[str, Any]:
											return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3					)

					@skip_mps
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[str]:
											return super().test_save_load_optional_components()

					@skip_mps
					def   lowerCamelCase__   (			self  :      Tuple					)			->							Any:
											return super().test_attention_slicing_forward_pass()






					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[Any]:
											super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



@slow
@require_torch_gpu
class       __A		(   unittest.TestCase			):

					def   lowerCamelCase__   (			self  :      Any					)			->							str:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()

					def   lowerCamelCase__   (			self  :      Tuple					)			->							Optional[int]:
											__magic_name__:	Tuple							=       torch_device

											__magic_name__:	Union[str, Any]							=       DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""					)
											__magic_name__:	Optional[Any]							=       pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	List[str]							=       torch.manual_seed(0					)
											__magic_name__:	Any							=       pipe(generator=__snake_case     ,		num_inference_steps=1_0_0     ,		audio_length_in_s=4.096					)
											__magic_name__:	Union[str, Any]							=       output.audios

											__magic_name__:	Optional[int]							=       audio[0, -3:, -3:]

											assert audio.shape == (1, 2, pipe.unet.sample_size)
											__magic_name__:	int							=       np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020]					)

											assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1E-2






					def   lowerCamelCase__   (			self  :      int					)			->							int:
											__magic_name__:	int							=       torch_device

											__magic_name__:	List[Any]							=       DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""     ,		torch_dtype=torch.floataa					)
											__magic_name__:	List[str]							=       pipe.to(__snake_case					)
											pipe.set_progress_bar_config(disable=__snake_case					)

											__magic_name__:	Tuple							=       torch.manual_seed(0					)
											__magic_name__:	List[Any]							=       pipe(generator=__snake_case     ,		num_inference_steps=1_0_0     ,		audio_length_in_s=4.096					)
											__magic_name__:	str							=       output.audios

											__magic_name__:	Optional[Any]							=       audio[0, -3:, -3:]

											assert audio.shape == (1, 2, pipe.unet.sample_size)
											__magic_name__:	Any							=       np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]					)

											assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1E-2








'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  None


SCREAMING_SNAKE_CASE__     :						Tuple	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {
    '''camembert-base''': 5_1_2,
}

SCREAMING_SNAKE_CASE__     :						Any	=  '''▁'''

class 							a__( snake_case__     ):
			a_	: Tuple									=      VOCAB_FILES_NAMES
			a_	: Optional[int]									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: List[str]									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: str									=      ['''input_ids''', '''attention_mask''']
			a_	: Union[str, Any]									=      CamembertTokenizer






			def __init__(     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="<unk>"					,   _UpperCAmelCase="<pad>"					,   _UpperCAmelCase="<mask>"					,   _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]					,   **_UpperCAmelCase					,   )							->							str:
					# Mask token behave like a normal word, i.e. include the space before it
					snake_case__	     =AddedToken(_UpperCAmelCase					,   lstrip=_UpperCAmelCase					,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else mask_token

					super().__init__(
					    _UpperCAmelCase					,   tokenizer_file=_UpperCAmelCase					,   bos_token=_UpperCAmelCase					,   eos_token=_UpperCAmelCase					,   sep_token=_UpperCAmelCase					,   cls_token=_UpperCAmelCase					,   unk_token=_UpperCAmelCase					,   pad_token=_UpperCAmelCase					,   mask_token=_UpperCAmelCase					,   additional_special_tokens=_UpperCAmelCase					,   **_UpperCAmelCase					,   )

					snake_case__	     =vocab_file
					snake_case__	     =False if not self.vocab_file else True






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:

					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					snake_case__	     =[self.cls_token_id]
					snake_case__	     =[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:
					snake_case__	     =[self.sep_token_id]
					snake_case__	     =[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[str]:
					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'						)

					if not os.path.isdir(_UpperCAmelCase						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
					snake_case__	     =os.path.join(
					    _UpperCAmelCase					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

					if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
							copyfile(self.vocab_file					,   _UpperCAmelCase						)

					return (out_vocab_file,)




